My research lies at the intersection of algorithms and machine learning. I am interested in developing novel optimization frameworks that are motivated by applications in machine learning.
In particular, my research focuses on data-driven algorithm design, combinatorial optimization, machine learning, and mechanism design. I develop novel models and algorithmic machinery to address modern challenges of decision-making. For example, I have been working on exponentially faster algorithms for submodular optimization. For more details, my publications can be found here.
I received my PhD in Computer Science from Harvard University where I was advised by Yaron Singer. My thesis was awarded an ACM SIGecom Doctoral Dissertation Honorable Mention. As a graduate student, I also received a Google PhD fellowship and a Smith Family Graduate Science and Engineering Fellowship. Prior to that, I received my BS degree in Mathematical Sciences and Computer Science from Carnegie Mellon University